Data Center Industry Research Articles

Humans in a Bottle: Material and Immaterial Ecosystems

Presenting their experiences as artists and researchers, Soft Turns question the western scientific compulsion to fully ‘know’ a subject, and the human desire to control one’s environment, which extends beyond the visible into the air around us. Noting parallels in the history of the internet and meteorology, they discuss examples of human intervention in thermodynamic systems such as the failed Biosphere 2 experiment, controlled environment agricultural and space travel research, and the energy intensive data centre industry. Critiquing capitalism’s dependence on extractive, exploitative growth in both material and immaterial dimensions, they argue that in our current climate emergency, investing in strategies to loosen western thinking and control is critical. With examples from their artistic production, they suggest that slowing down, learning from other ways of knowing the world, and looking to plants and other intelligences can all contribute to a reactivation of an empathetic, intimate engagement with our environments.

Carbon emission scenario analysis of data centers in China under the carbon neutrality target

The low-carbon transformation of data centers is of great significance to achieve the goals of carbon peaking and carbon neutrality. This study compared and analyzed the overall situation of data centers in China. Based on China's CO2 emission and intensity targets in key years, the four variables of energy efficiency improvement rate, nonfossil energy consumption proportion, negative emission technology intensity, and waste energy utilization rate were introduced, and a net zero emission path model of data centers was established. Using scenario analysis to predict the total CO2 emissions and emission intensity from 2021 to 2060, three emission reduction path scenarios were obtained. Results showed that the energy consumption of data centers increased gradually, the carbon emissions first increased and then decreased, and the power usage effectiveness (PUE) of the data centers decreased gradually. The carbon peak time of the three scenarios is 2030, and the time for carbon neutrality is 2055, 2053, and 2051 in three scenarios. The data center industry should further improve the energy efficiency utilization rate, increase the proportion of nonfossil energy consumption, strengthen the technological innovation of carbon capture and storage, enhance the level of carbon sink, and optimize the utilization rate of waste energy.

Smarter, greener extractivism: digital infrastructures and the harnessing of new resources

ABSTRACT The past several years have seen increased scholarly attention to the concept of ‘extraction’ and ‘extractivism’ as critical frameworks in the humanities and social sciences. These are not only concepts and processes through which to understand material extraction but also expanded formations of extraction as requiring an assemblage of interlocking activities united under an umbrella of exploitative, material economic practices. This article demonstrates that digital data infrastructures, especially data centres, are acting as tools in which to manage the compounding contradictions of paired ‘green,’ digital growth in constrained electricity systems like Ireland. Building on theories of ‘green extractivism’ in the digital sphere and drawing upon fieldwork and policy analysis in data centre industry settings, this article argues that in the form of interconnected climate and digital infrastructures, tech capital is shoring up its role in green transformations, including in the grid systems that will need to adapt away from fossil fuel supply to intermittent energy sources and increasing demand from data centres. In this way, data centres are becoming technologies of green extractivism for overlapping projects of digital grid transformations and climate-friendly capitalism.

Enhancing heat transfer processes in 2U-Rack Servers: A study case employing thermal simulation and the Taguchi method

The data center industry consumes large quantities of energy due to the consumption spawned by Information Technology Equipment (ITE) and air conditioning systems. Servers constitute the predominant portion of ITE within a data center. Relatedly, diverse optimization strategies are required to minimize data centers' energy consumption. This paper used a 2U-Rack Server, specifically, the R261-3C0 server as a study case to enhance heat transfer processes by minimizing the temperature variation in the high thermal-stress components and optimizing airflow patterns. The Taguchi Method is used for this purpose. A thermal simulation is conducted using Computational Fluid Dynamics (CFD) techniques through the Ansys Fluent code. Seven design variables were proposed as control factors, submitting them to the noise factors. New parameters such as the motherboard lifting, the displacement of heatsinks and the CPUs, and the fan speed variation will be introduced among these control factors. Among the noise factors, we considered the fluid velocity at the server inlet region and the Operating Point of the Fan (OPF). After applying the Taguchi Method, the control factors and their optimal levels were obtained. The thermal simulation presented a consistent performance. The optimal configuration of the control parameters generated a 10 °C decrease in the server's overall temperature, an air circulation enhancement by the vector field behavior verification, and a 17.11 % decrease in the server's thermal variation. The optimized model stays in its verification phase, and the validation phase should be accomplished in future work.

The Impact of Artificial Intelligence on the Modern Data Center Industry

The Impact of Artificial Intelligence on the Modern Data Center Industry

The Impact of Artificial Intelligence on the Modern Data Center Industry

The Impact of Artificial Intelligence on the Modern Data Center Industry

Analysis on data center power supply system based on multiple renewable power configurations and multi-objective optimization

With rapid development of data center industry, achieving low energy consumption and costs become important. How to provide an optimal configuration on renewable distributed energy systems combining data centers is challenging. This work discussed possible hybrid configurations by combining diesel, photovoltaic, wind, and battery capacity in data center. For each mode, renewable penetration (RP) and the Levelized Cost of Electricity (LCOE) were used to evaluate the performance of system. A multi-objective optimization analysis of the case is performed based on the entropy weighted-TOPSIS method, genetic algorithm, and NSGA-II aiming to obtain both a high RP and a low LCOE. Then, comparative research on optimal power configuration with conventional or renewable energy was conducted. The results show that it is recommended to take a high-rated PV power. When wind power is taken, it is only meaningful when combined with PV power system. For the hybrid renewable power system without an energy storage unit, it's easy to realize a lower LCOE compared to Diesel mode, and its realizable maximum RP is 28.31 %. When battery energy storage is taken in the hybrid system, six optimal configurations are recommended which can achieve the lowest cost of 0.21 $/kWh and the highest RP of 44.5 %.

Dynamic thermal environment management technologies for data center: A review

The energy demand of the data center (DC) industry has accounted for 2% of the total global energy consumption, and its operating power consumption has reached 50 times that of the commercial buildings. Therefore, improving the thermal environment performance to reduce the energy cost is major trend to realize the energy savings of DC. It has become a positive measure to integrate smart technologies to improve the operating efficiency of HVAC systems, especially due to the development of informatization and intelligence. This study provides detailed overview of the dynamic thermal environment management technologies of the DCs. The requirements of thermal environment in DCs were reviewed by different levels, which are the basis of the design and operation. Accordingly, various dynamic prediction technologies were presented concerning the theoretical mechanics, the computation efficiency and the applicable circumstances. The performance of the PID, the model predictive control and the reinforcement learning in the dynamic thermal environment control of DC were analyzed. Moreover, the evaluation systems based on cooling capacity, exergy analysis and entransy analysis were introduced respectively to measure the performance of the thermal environment. By investigating the knowledge related to the “requirement, prediction, control, and evaluation” of the DC dynamic thermal environment, this study aims to provide reference and guidance for energy conservation, airflow organization and smart operation of DCs.

Operations of data centers with onsite renewables considering greenhouse gas emissions

In a highly technology-dependent society, processing large amounts of data has become essential, resulting in a growing number of data centers. The global data center industry accounts for a significant proportion of the world’s energy consumption. A partial or total transition of the energy sources that power the operation of data centers to cleaner energy, particularly onsite renewable technologies, will help to achieve the greenhouse gas emission reduction goals established in international agreements. However, integrating such technologies is difficult due to the intermittency and availability of renewable sources. Nevertheless, temporal and geographical flexibility for processing part of the data centers’ workload provides an opportunity to efficiently integrate these renewable energy sources. In this paper, we develop an operational scheme for a network of interconnected data centers with a significant electricity supply from onsite technologies consisting of (i) an optimal job migration and scheduling policy for data centers to match their operation and the availability of renewable energy under an emissions cap-and-trade program; (ii) an energy management system to control the data centers’ onsite renewable and grid energy resources; and (iii) a robust optimization approach to account for the intermittency of the renewables and to identify the optimal migration and scheduling policies under the worst-case scenario. A case study is carried out to assess the economic and environmental impacts. The results indicate that the installation of onsite technologies and an optimal migration policy reduce the operational costs and the greenhouse gas emissions of the networks of data centers.

Modeling and Thermal Simulation of 2U-Rack Servers Applying Computational Fluid Dynamics Techniques

The data center industry consumes between 196 and 400 terawatt-hours annually, between 1% and 2% of the world's energy consumption. A high percentage of data center energy consumption is associated with air conditioning and cooling systems. The optimization of the data center cooling process can occur at multiple scales, being one of the most relevant to the generation and heat transfer processes within servers. In this approach, we must study the temperature fields of each server’s components and understand airflow behavior within the server chassis. The present work, in its first stage, performs a thermal and cooling analysis for a two-unit rack server (2U-Rack) using computational fluid dynamics (CFD) techniques via Ansys Fluent code. A geometric server model was developed, considering heat generation in the main electrical components and heat and air mass transfer with the data center. The model was validated by comparing simulation results with the server's sensor values and the results of thermography testing. Following CFD simulation, airflow velocity fields, temperature contours, and sensitivity scenarios were obtained, with the goal of proposing an optimization model that would allow us to improve server heat transfer processes in the following steps.

A dumbbell-shaped 3D flat plate pulsating heat pipe module augmented by capillarity gradient for high-power server CPU onsite cooling

With the development of the data center industry and the increasing power density of servers, it is crucial to remove waste heat from small-area heat sources to ensure the safe and efficient operation of data processing chips. To complement existing studies, in this paper, a novel three-dimensional flat plate aluminum pulsating heat pipe (3D-FPPHP) with four different channel structures for radial heat dissipation of high-power server chips was proposed. The results showed that under the favorable influence of the additional capillarity force provided by the variable diameter channels, the FPPHP filled with acetone can start up easily and has good robustness during variable power operation. When the input power was 150 W, the junction temperature of the heating source could be reduced by 42.49 °C (48.67%) compared to an identical aluminum plate. In addition, a heat sink module based on the proposed 3D-FPPHP with optimal charging ratio was tested in a 1U server chassis. The results showed that it can manage an input power of 300 W (corresponding to a heat flux of 41.15 W/cm2) and achieve a total thermal resistance of 0.2053 °C/W under the premise of ensuring a safe junction temperature of chips below 85 °C.

Performance and sustainability assessment of PEMFC/solar-driven CCP systems with different energy storage devices

The rise in cloud computing has promoted the rapid development of the data center industry. Proton exchange membrane fuel cells have high energy conversion efficiency and the potential to be applied in data center applications. In this study, by considering different energy storage devices and multienergy complementarity, four types of proton exchange membrane fuel cell-driven cooling-power systems were designed for data center applications. The effects of different types of energy storage devices and combination of solar energy on the operation characteristics were systematically analyzed. The results indicate that among the four cooling-power systems, the one with heat storage and solar thermal collector provides the best matching between cooling/power supplies and demands. The systems with a heat storage device consumed more hydrogen (3.6%) than those with a power storage device because of the larger requirement of heat (3.8%) and higher current density in the proton exchange membrane fuel cell. Results of emergy analysis show that the cooling-power system with power storage and solar thermal collector reached a maximum environmental sustainability index of 57.610 and a minimum environmental load ratio of 0.018, which imply strong sustainability potential while releasing the lowest pressure to the environment compared to the other systems.

Carbon awareness oriented data center location and configuration: An integrated optimization method

Serving as the information backbone of an increasingly digitalized world and energy-intensive, the data center industry has to seek low-carbon developing way to properly meet the rapidly rising service demand. This paper designs 45 kinds of configuration choices for each decision units by integrating three data centers, three cooling technologies, as well as five energy supply mixes. The model is formulated as a bi objective optimization problem for the decision maker to decide newly established data location and configuration with objective of reducing data center expansion costs and well as minimizing power utilization associated with carbon emissions from a country level. The pilot study in China under the scenarios of different carbon emissions limitation and propagation delay that China can reduce at least 25% of newly established data center related emissions from 2020 to 2023. It further shows that future expansion can be focus on Inner Mongolia and Ningxia for the north, Hubei for the middle, and Yunnan and Guangxi for the south. This paper provides a fresh perspective and makes a valuable attempt at the low-carbon development of data centers.

Iceland warms to data [data centre industry

Life on Iceland is chilly, foggy and windy all year round. And while they may have to wrap up warm, and are unlikely to get a tan, Icelanders have acknowledged that their natural cold climate is ideal for one particular industry. The island has pitched itself as providing the most sustainable and cost-efficient data centres in the world. E&T took a tour of some of the most notable to find out if this is the case.

A material social view on data center waste heat: Novel uses and metrics

Today's data centers use substantial amounts of the world's electrical supply. However, in line with circular economy concepts, much of this energy can be reused. Such reuse includes the heating of buildings, but also commodity dehydration, electricity production and energy storage. This multi-disciplinary paper presents several novel applications for data center waste heat. Next, the paper accounts for three case studies, taken from three different societal contexts: urban Malaysia, rural Costa Rica and semi-urban Sweden. A discussion on data center energy metrics leads to the development of a new metric, Datacenter Energy Sustainability Score (DESS), which is evaluated within the three use cases. Last, it is shown how a material social view on metrics provides a way past a problem that has haunted the data center industry for the last 15 years, whilst benefitting both data center owners who want to compete through sustainability as well as stakeholders from governments on local, regional and national levels. The paper makes clear that a sustainability strategy should be based on a material social view and stretch beyond the building itself. In fact, and as demonstrated by the relevance of DESS, modern data centers are so energy-efficient that data center sustainability is no longer mainly an engineering issue, but a matter requiring multi-disciplinary insights, approaches and collaboration.

This site is a dead end? Employment uncertainties and labor in data centers

Would technological changes increase the need for human workers or eliminate them altogether? This uncertainty has produced an unresolved tension, from the industrial revolution to the rise of the information society. The data center industry has been largely invisible in public debates about this question. Yet the same tensions exist within the industry itself: Will automation create data center jobs or kill them? In this article, we work inside the “black box” – the data center, to examine uncertainties faced by those who work there. We do so through interviews and observations, first, of data center managers and executives at international trade expos, where anxieties about the shortage of data center workers but also their irrelevance were palpable. Then, we turn to a remote data center in Finland, where security guards and technical operators negotiate employment uncertainties through the biopolitics of their labor. In both sites, the uncertainties about data center employment are manifest and embodied, even if they are expressed and experienced in different ways. On both the top and bottom levels of data center hierarchies, people are discomfited by the possibility of their own redundancy. At the same time, they present the sunnier sides of data center work when they talked about their efforts to resolve ongoing issues of worker shortage, the lack of diversity in data centers, and the routines that could easily slide into boredom or anomie. We situate our findings on the long arc of capitalist transformations and discuss the insights they might provide for today’s data-driven economy in general.

Data Centers and Green Energy: Paving the Way for a Sustainable Digital Future

As Information and Communication Technologies (ICTs) reach unprecedented heights of global influence, the USA stands as a leader with 91.8% of its residents connected to the internet. The interconnection of sectors like healthcare, finance, social media, and governance through the internet results in an immense volume of data, stored and managed globally within Data Centers. The surge in Data Centers in the USA, driven by a 4.12% CAGR and expanding service providers, prompts a critical examination of their environmental impact. Currently, nearly 2% of the USA's total energy production is attributed to Data Centers, predominantly reliant on conventional energy sources. This research explores the potential transition of Data Centers to green energy sources, investigating the feasibility of taking them off the grid. By aligning internet user growth, Data Center functionalities, and the USA's renewable energy capacity, the study aims to unravel the environmental implications and sustainability challenges faced by the Data Center industry. Through an in-depth analysis of key point indicators for Data Center sustainability and leveraging various renewable resources, the research assesses the viability of supporting Data Centers with green energy in the USA. The findings delve into predictions for renewable energy production, offering insights into aligning energy consumption patterns with sustainable practices for a greener digital future.

Why Fuel Cells Could Help Power a Greener Data Centre Industry

Abstract For the power–hungry data centre industry, going green has rapidly become an all–important business imperative, writes Roberto Castaldini, Offering Specialist for AC Power at Vertiv in EMEA.

Storing data on the margins: making state and infrastructure in Southwest China

ABSTRACT Examining the emerging data center industry in Guizhou, Southwest China, this article investigates the infrastructure-making processes that are initiated to implement cloud infrastructures, and how they are mobilized to reconfigure Guizhou’s nature. It discusses how these processes have come about in tandem with the expansion of China’s cloud geography, and how they are impacting the region. Contextualizing and historicizing these processes, this article argues that the developing data center industry in Guizhou is part of the broader process of state-building. These processes of implementing cloud infrastructure in Guizhou lead to the co-production of further state legitimation and continued marginalization of Guizhou, thus calling into question the common claim that technology bridges economic disparities and enhances connectivity.

Performance analysis of an energy-saving strategy in cloud data centers based on a MMAP[K]/M[K]/[formula omitted] non-preemptive priority queue

With the development of cloud computing, big data, artificial intelligence and other next generation information technologies, the scale of the data center industry worldwide is growing rapidly, resulting in a sharp increase in energy consumption. In order to efficiently reduce the idle energy consumption in cloud data centers, in this paper, we propose an energy-saving strategy based on two-threshold hysteresis cluster scheduling mechanism, which enables the reserved cluster to be dynamically switched on and off with load variation. In addition, the tasks to be processed are classified into real-time tasks and non-real-time tasks. To keep track of the two classes of tasks and model the correlated traffic in cloud data centers, we describe the arrival flow of tasks as a Marked Markovian Arrival Process (MMAP). Accordingly, we develop a non-preemptive priority queue as the system model to capture the working principle of the proposed strategy. By using the matrix-geometric solution and Gauss–Seidel method, the steady-state distribution of the system model is analyzed, and some key Quality of Service (QoS) metrics and Total Cost of Ownership (TCO) metrics are calculated. Results of numerical experiments show that, under the proposed energy-saving strategy, the overall power consumption in a small cloud data center can be reduced by an average of 30.20% under different scenarios, and the larger the cloud data center scale, the more obvious the energy saving effect. The results also confirm the impact of inter-class correlations of the input process on performance metrics. Furthermore, we identify the Pareto optimal solutions for trading off the overall power consumption and average waiting time of real-time tasks.